class_docstr: Convert a collection of raw documents to a matrix of text-index
  features
  
  keep the order of word in document
  index start with 1, index 0 is default padding_value
  
  Parameters
  ----------
  
  embedding_size: size of embedding word to vector
code: def __init__(
      self,
      embedding_size=100,
      padding=False,
      padding_value=0,
      unknown_value=1,
  ):
      self.vocab = []
      self.embedding_size = embedding_size
      self.padding = padding
      self.padding_value = padding_value
      self.unknown_value = unknown_value
  
      self.max_length = 0
      self.ns = 2
  
file_path: underthesea/underthesea/transformer/word_vector.py
imports: from functools import reduce
  from underthesea.transformer import Transformer
label_desc: Use image node_id 1 to create a new WordVectorTransformer object from inherited base classes: Transformer with example: obj = WordVectorTransformer(embedding_size, padding, padding_value, unknown_value)